Give advice about good practices when building R packages. Advice includes functions and syntax to avoid, package structure, code complexity, code formatting, etc.

This package provides a framework for analytically computing the asymptotic confidence intervals and maximum-likelihood estimates of a class of continuous-time Gaussian branching processes defined by Mitov V, Bartoszek K, Asimomitis G, Stadler T (2019) <doi:10.1016/j.tpb.2019.11.005>. The class of model includes the widely used Ornstein-Uhlenbeck and Brownian motion branching processes. The framework is designed to be flexible enough so that the users can easily specify their own sub-models, or re-parameterizations, and obtain the maximum-likelihood estimates and confidence intervals of their own custom models.

Variable selection for ultrahigh-dimensional ("large p small n") linear Gaussian models using a fiducial framework allowing to draw inference on the parameters. Reference: Lai, Hannig & Lee (2015) <doi:10.1080/01621459.2014.931237>.

Analysis of multivariate data using generalized linear latent variable models (gllvm). Estimation is performed using either the Laplace method, variational approximations, or extended variational approximations, implemented via TMB (Kristensen et al. (2016), <doi:10.18637/jss.v070.i05>).

Focused on extracting important data from track points such as speed, distance, elevation difference and azimuth.(PLAZA, J. et al., 2022) <doi:10.1016/j.applanim.2022.105643>.

Generalized estimating equations with the original sandwich variance estimator proposed by Liang and Zeger (1986), and eight types of more recent modified variance estimators for improving the finite small-sample performance.

This package provides functions are provided for estimation, testing, diagnostic checking and forecasting of generalized linear autoregressive moving average (GLARMA) models for discrete valued time series with regression variables. These are a class of observation driven non-linear non-Gaussian state space models. The state vector consists of a linear regression component plus an observation driven component consisting of an autoregressive-moving average (ARMA) filter of past predictive residuals. Currently three distributions (Poisson, negative binomial and binomial) can be used for the response series. Three options (Pearson, score-type and unscaled) for the residuals in the observation driven component are available. Estimation is via maximum likelihood (conditional on initializing values for the ARMA process) optimized using Fisher scoring or Newton Raphson iterative methods. Likelihood ratio and Wald tests for the observation driven component allow testing for serial dependence in generalized linear model settings. Graphical diagnostics including model fits, autocorrelation functions and probability integral transform residuals are included in the package. Several standard data sets are included in the package.

Simulation tool to facilitate determination of required sample size to achieve category saturation for studies using multiple repertory grids in conjunction with content analysis.

Interact with the Google Tag Manager API <https://developers.google.com/tag-platform/tag-manager/api/v2>, enabling scripted deployments and updates across multiple tags, triggers, variables and containers.

Perform gene set enrichment analyses using the Gene set Ordinal Association Test (GOAT) algorithm and visualize your results. Koopmans, F. (2024) <doi:10.1038/s42003-024-06454-5>.

This package provides classes for GeoJSON to make working with GeoJSON easier. Includes S3 classes for GeoJSON classes with brief summary output, and a few methods such as extracting and adding bounding boxes, properties, and coordinate reference systems; working with newline delimited GeoJSON'; and serializing to/from Geobuf binary GeoJSON format.

This package provides tools for estimating forest metrics such as stem volume, biomass, and carbon using regional allometric equations. The package implements widely used models including Dagnelie P., Rondeux J. & Palm R. (2013, ISBN:9782870161258) "Cubage des arbres et des peuplements forestiers - Tables et equations" <https://orbi.uliege.be/handle/2268/155356>, Vallet P., Dhote J.-F., Le Moguedec G., Ravart M. & Pignard G. (2006) "Development of total aboveground volume equations for seven important forest tree species in France" <doi:10.1016/j.foreco.2006.03.013>, Pauwels D. & Rondeux J. (1999, ISSN:07779992) "Tarifs de cubage pour les petits bois de meleze (Larix sp.) en Ardenne" <https://orbi.uliege.be/handle/2268/96128>, Massenet J.-Y. (2006) "Chapitre IV: Estimation du volume" <https://jymassenet-foret.fr/cours/dendrometrie/Coursdendrometriepdf/Dendro4-2009.pdf>, France Valley (2025) "Bilan Carbone Forestier - Methodologie" <https://www.france-valley.com/hubfs/Bilan%20Carbone%20Forestier.pdf>. Its modular structure allows transparent integration of bibliographic or user-defined allometric relationships.

Combining a generalized linear model with an additional tree part on the same scale. A four-step procedure is proposed to fit the model and test the joint effect of the selected tree part while adjusting on confounding factors. We also proposed an ensemble procedure based on the bagging to improve prediction accuracy and computed several scores of importance for variable selection. See Cyprien Mbogning et al.'(2014)<doi:10.1186/2043-9113-4-6> and Cyprien Mbogning et al.'(2015)<doi:10.1159/000380850> for an overview of all the methods implemented in this package.

This package provides tools for specifying and evaluating standard and truncated probability distributions, with support for log-space computation and joint distribution specification. It enables Bayesian computation for cognition models and includes utilities for density calculation, sampling, and visualisation, facilitating prior distribution specification and model assessment in hierarchical Bayesian frameworks.

Fetch Professional Golfers Association (PGA) Tour tournament data from ESPN <https://www.espn.com/golf/> including leaderboards and hole-by-hole scoring. Data is returned in tidy tibble format ready for analysis. Supports local storage via RDS or Apache Arrow Parquet files for fast repeated access. Designed for golf analytics, data journalism, and fantasy sports research.

Add vector field layers to ggplots. Ideal for visualising wind speeds, water currents, electric/magnetic fields, etc. Accepts data.frames, simple features (sf), and spatiotemporal arrays (stars) objects as input. Vector fields are depicted as arrows starting at specified locations, and with specified angles and radii.

Extremely efficient procedures for fitting regularization path with l0, l1, and truncated lasso penalty for linear regression and logistic regression models. This version is a completely new version compared with our previous version, which was mainly based on R. New core algorithms are developed and are now written in C++ and highly optimized.

Fit joint models of survival and multivariate longitudinal data. The longitudinal data is specified by generalised linear mixed models. The joint models are fit via maximum likelihood using an approximate expectation maximisation algorithm. Bernhardt (2015) <doi:10.1016/j.csda.2014.11.011>.

Generalized additive model selection via approximate Bayesian inference is provided. Bayesian mixed model-based penalized splines with spike-and-slab-type coefficient prior distributions are used to facilitate fitting and selection. The approximate Bayesian inference engine options are: (1) Markov chain Monte Carlo and (2) mean field variational Bayes. Markov chain Monte Carlo has better Bayesian inferential accuracy, but requires a longer run-time. Mean field variational Bayes is faster, but less accurate. The methodology is described in He and Wand (2024) <doi:10.1007/s10182-023-00490-y>.

Reads annual financial reports including assets, liabilities, dividends history, stockholder composition and much more from Bovespa's DFP, FRE and FCA systems <http://www.b3.com.br/pt_br/produtos-e-servicos/negociacao/renda-variavel/empresas-listadas.htm>. These are web based interfaces for all financial reports of companies traded at Bovespa. The package is specially designed for large scale data importation, keeping a tabular (long) structure for easier processing.

Spatio-temporal radial basis functions (optimization, prediction and cross-validation), summary statistics from cross-validation, Adjusting distance-based linear regression model and generation of the principal coordinates of a new individual from Gower's distance.

Robust regression via gamma-divergence with L1, elastic net and ridge.

Supports the assessment of functional enrichment analyses obtained for several lists of genes and provides a workflow to analyze them between two species via weighted graphs. Methods are described in Sosa et al. (2023) <doi:10.1016/j.ygeno.2022.110528>.

The American Community Survey (ACS) <https://www.census.gov/programs-surveys/acs> offers geodatabases with geographic information and associated data of interest to researchers in the area. The goal of this package is to generate objects that allow us to access and consult the information available in various formats, such as in GeoPackage format or in multidimensional ROLAP (Relational On-Line Analytical Processing) star format.